Palavras-chave
Desempenho escolar
Avaliação do aluno
Valor agregado
Como Citar
Resumo
Esta pesquisa apresenta um modelo para analisar e prever o desempenho em habilidades de raciocínio quantitativo em estudantes de engenharia na Colômbia. A população estudada incluiu 12.411 estudantes de engenharia no ano de 2020. Foram utilizadas como variáveis de entrada as competências em matemática, ciências, inglês, leitura e ciências sociais obtidas no teste padronizado SABER 11, enquanto a variável de resposta foi o desempenho em raciocínio quantitativo do teste SABER PRO. Foi realizada uma análise descritiva considerando as variáveis de gênero, regime escolar e situação profissional dos estudantes. Posteriormente, foi implementado um modelo de floresta aleatória, identificando que as competências em matemática e biologia são as que têm maior impacto parcial na previsão do desempenho em raciocínio quantitativo. O modelo preditivo atingiu um RMSE de 10,95 e um R² de 69%, demonstrando sua capacidade de prever efetivamente o desempenho nessa competência-chave.
Aparicio, J., Cordero, J. M., & Ortiz, L. (2019). Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates. Socio-Economic Planning Sciences, 68, 100698. https://doi.org/10.1016/j.seps.2019.03.004
Berens, J., Schneider, K., Gortz, S., Oster, S., & Burghoff, J. (2019). Early Detection of Students at Risk—Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods. Journal of Educational Data Mining, 11(3), 1-41. https://eric.ed.gov/?id=EJ1241620
Bosch, E., Seifried, E., & Spinath, B. (2021). What successful students do: Evidence-based learning activities matter for students’ performance in higher education beyond prior knowledge, motivation, and prior achievement. Learning and Individual Differences, 91, 102056. https://doi.org/10.1016/j. lindif.2021.102056
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
Columbu, S., Porcu, M., & Sulis, I. (2021). University choice and the attractiveness of the study area: Insights on the differences amongst degree programmes in Italy based on generalised mixed-effect models. Socio-Economic Planning Sciences, 74, 100926. https://doi.org/10.1016/j.seps.2020.100926
Delahoz-Dominguez, E. J., Guillen-Ibarra, & Fontalvo-Herrera, T. (2020a). Análisis de la acreditación de calidad en programas de ingeniería industrial y los resultados en las pruebas nacionales estandarizadas, en Colombia. Formación Universitaria, 13(1), 127-134. https://doi.org/10.4067/S0718 50062020000100127
Delahoz-Dominguez, E., Zuluaga, R., & Fontalvo-Herrera, T. (2020b). Dataset of academic performance evolution for engineering students. Data in Brief, 30, 105537. https://doi.org/10.1016/j.dib.2020.105537
Eichler, A., & Gradwohl, J. (2021). Investigating Motivational and Cognitive Factors which Impact the Success of Engineering Students. International Journal of Research in Undergraduate Mathematics Education, 7(3), 417-437. https://doi.org/10.1007/s40753-020-00127-4
Gasevic, D., Rose, C., Siemens, G., Wolff, A., & Zdrahal, Z. (2014). Learning analytics and machine learning. En S. Teasley, & A. Pardo (Eds.), LAK 2014: Fourth International Conference on Learning Analytics and Knowledge (pp. 287-288). Association for Computing Machinery (ACM). https://doi.org/10.1145/2567574.2567633
Gwelo, A. S. (2019). Determinants of career choice among university students. MOJEM: Malaysian Online Journal of Educational Management, 7(1), Article 1.
Hart, P. F., & Rodgers, W. (2024). Competition, competitiveness, and competitive advantage in higher education institutions: A systematic literature review. Studies in Higher Education, 49(11), 2153-2177. https://doi.org/10.1080/03 075079.2023.2293926
Heidrich, L., Victória Barbosa, J. L., Cambruzzi, W., Rigo, S. J., Martins, M. G., & dos Santos, R. B. S. (2018). Diagnosis of learner dropout based on learning styles for online distance learning. Telematics and Informatics, 35(6), 1593 1606. https://doi.org/10.1016/j.tele.2018.04.007
Kamal, N., Sarker, F., Rahman, A., Hossain, S., & Mamun, K. A. (2024). Recommender System in Academic Choices of Higher Education: A Systematic Review. IEEE Access, 12, 35475-35501. https://doi.org/10.1109/ACCESS. 2024.3368058
Kongara, D., & Sathyanarayana, N. (2019). Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction. International Journal of Intelligent Engineering and Systems, 12, 30-39. https://doi.org/10.22266/ijies2019.0228.04
Londoño-Vélez, J., Rodríguez, C., & Sánchez, F. (2020). Upstream and Downstream Impacts of College Merit-Based Financial Aid for Low-Income Students: Ser Pilo Paga in Colombia. American Economic Journal: Economic Policy, 12(2), 193-227. https://doi.org/10.1257/pol.20180131
Mayes, R., Long, T., Huffling, L., Reedy, A., & Williamson, B. (2020). Undergraduate Quantitative Biology Impact on Biology Preservice Teachers. Bulletin of Mathematical Biology, 82(6), 63. https://doi.org/10.1007/s11538-020-00740-z
McLaughlin, J. E., Morbitzer, K., Meilhac, M., Poupart, N., Layton, R. L., & Jarst fer, M. B. (2023). Standards needed? An exploration of qualifying exams from a literature review and website analysis of university-wide policies. Studies in Graduate and Postdoctoral Education, 15(1), 19-33. https://doi.org/10.1108/SGPE-11-2022-0073
Nachouki, M., Mohamed, E. A., Mehdi, R., & Abou Naaj, M. (2023). Student course grade prediction using the random forest algorithm: Analysis of predictors’ importance. Trends in Neuroscience and Education, 33, 100214. https://doi.org/10.1016/j.tine.2023.100214
Oates, B. J. (2005). Researching information systems and computing. Sage.
Salazar-Fernandez, J. P., Sepúlveda, M., & Munoz-Gama, J. (2019). Influence of Student Diversity on Educational Trajectories in Engineering High-Failure Rate Courses that Lead to Late Dropout. 2019 IEEE Global Engineering Education Conference (EDUCON), Dubai, United Arab Emirates, pp. 607-616. https://doi.org/10.1109/EDUCON.2019.8725143
Soto-Acevedo, M., Abuchar-Curi, A. M., Zuluaga-Ortiz, R. A., & Delahoz-Dominguez, E. J. (2023). A Machine Learning Model to Predict Standardized Tests in Engineering Programs in Colombia. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje, 18(3), 211-218. https://doi.org/10.1109/rita.2023.3301396
Tossavainen, T., Rensaa, R. J., Haukkanen, P., Mattila, M., & Johansson, M. (2021). First-year engineering students’ mathematics task performance and its relation to their motivational values and views about mathematics. European Journal of Engineering Education, 46(4), 604-617. https://doi.org/10.1080/03043797.2020.1849032
Visbal-Cadavid, D., Martínez-Gómez, M., & Guijarro, F. (2017). Assessing the efficiency of public universities through DEA. A case study. Sustainability, 9(8), 1416.
Wright, M. N., & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 077(i01). https://econpapers.repec.org/article/jssjstsof/v_3a077_3ai01.htm
Zakariya, Y. F., Nilsen, H. K., Bjørkestøl, K., & and Goodchild, S. (2023). Analysis of relationships between prior knowledge, approaches to learning, and mathematics performance among engineering students. International Journal of Mathematical Education in Science and Technology, 54(6), 1015-1033. https://doi.org/10.1080/0020739X.2021.1984596
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Copyright (c) 2025 Enrique Delahoz-Dominguez, Rohemi Zuluaga-Ortiz, Carlos García-Yerena